Overview

Dataset statistics

Number of variables12
Number of observations3428
Missing cells0
Missing cells (%)0.0%
Duplicate rows419
Duplicate rows (%)12.2%
Total size in memory321.5 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

Dataset has 419 (12.2%) duplicate rowsDuplicates
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual sugar and 3 other fieldsHigh correlation
alcohol is highly overall correlated with chlorides and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-04-13 21:03:38.999738
Analysis finished2023-04-13 21:03:47.455307
Duration8.46 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct65
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8638273
Minimum3.8
Maximum11.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:47.504022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.3
median6.8
Q37.3
95-th percentile8.3
Maximum11.8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84756377
Coefficient of variation (CV)0.12348268
Kurtosis1.2911622
Mean6.8638273
Median Absolute Deviation (MAD)0.5
Skewness0.58889796
Sum23529.2
Variance0.71836434
MonotonicityNot monotonic
2023-04-13T17:03:47.562742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 214
 
6.2%
6.8 212
 
6.2%
6.4 183
 
5.3%
6.7 175
 
5.1%
6.9 171
 
5.0%
7 163
 
4.8%
6.5 161
 
4.7%
7.2 147
 
4.3%
7.1 140
 
4.1%
6.3 133
 
3.9%
Other values (55) 1729
50.4%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
4.2 1
 
< 0.1%
4.4 2
 
0.1%
4.5 1
 
< 0.1%
4.7 3
 
0.1%
4.8 8
 
0.2%
4.9 5
 
0.1%
5 14
0.4%
5.1 17
0.5%
5.2 21
0.6%
ValueCountFrequency (%)
11.8 1
 
< 0.1%
10.7 2
 
0.1%
10.3 2
 
0.1%
10.2 1
 
< 0.1%
10 3
0.1%
9.9 2
 
0.1%
9.8 6
0.2%
9.7 4
0.1%
9.6 5
0.1%
9.5 1
 
< 0.1%

volatile acidity
Real number (ℝ)

Distinct115
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27923425
Minimum0.08
Maximum1.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:47.624067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.15
Q10.21
median0.26
Q30.33
95-th percentile0.46
Maximum1.1
Range1.02
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.10159289
Coefficient of variation (CV)0.36382677
Kurtosis5.8466227
Mean0.27923425
Median Absolute Deviation (MAD)0.06
Skewness1.6565846
Sum957.215
Variance0.010321116
MonotonicityNot monotonic
2023-04-13T17:03:47.682880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 186
 
5.4%
0.24 173
 
5.0%
0.22 172
 
5.0%
0.26 168
 
4.9%
0.2 157
 
4.6%
0.23 145
 
4.2%
0.25 144
 
4.2%
0.27 143
 
4.2%
0.3 132
 
3.9%
0.19 124
 
3.6%
Other values (105) 1884
55.0%
ValueCountFrequency (%)
0.08 3
 
0.1%
0.085 1
 
< 0.1%
0.1 2
 
0.1%
0.105 4
 
0.1%
0.11 12
 
0.4%
0.115 2
 
0.1%
0.12 18
0.5%
0.125 3
 
0.1%
0.13 31
0.9%
0.14 37
1.1%
ValueCountFrequency (%)
1.1 1
< 0.1%
1.005 1
< 0.1%
0.965 1
< 0.1%
0.93 1
< 0.1%
0.91 1
< 0.1%
0.905 1
< 0.1%
0.85 1
< 0.1%
0.815 1
< 0.1%
0.785 1
< 0.1%
0.78 1
< 0.1%

citric acid
Real number (ℝ)

Distinct82
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33330513
Minimum0
Maximum1.66
Zeros14
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:47.744197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.27
median0.32
Q30.38
95-th percentile0.53
Maximum1.66
Range1.66
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.1204905
Coefficient of variation (CV)0.36150209
Kurtosis7.7439389
Mean0.33330513
Median Absolute Deviation (MAD)0.06
Skewness1.4081982
Sum1142.57
Variance0.014517961
MonotonicityNot monotonic
2023-04-13T17:03:47.801539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 209
 
6.1%
0.28 205
 
6.0%
0.32 185
 
5.4%
0.34 162
 
4.7%
0.31 151
 
4.4%
0.27 151
 
4.4%
0.26 149
 
4.3%
0.29 145
 
4.2%
0.49 141
 
4.1%
0.33 123
 
3.6%
Other values (72) 1807
52.7%
ValueCountFrequency (%)
0 14
0.4%
0.01 5
 
0.1%
0.02 5
 
0.1%
0.04 9
0.3%
0.05 2
 
0.1%
0.06 3
 
0.1%
0.07 10
0.3%
0.08 3
 
0.1%
0.09 6
0.2%
0.1 10
0.3%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 5
 
0.1%
0.91 1
 
< 0.1%
0.88 1
 
< 0.1%
0.82 1
 
< 0.1%
0.8 1
 
< 0.1%
0.79 2
 
0.1%
0.78 1
 
< 0.1%
0.74 23
0.7%

residual sugar
Real number (ℝ)

Distinct286
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4193116
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:47.863329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.7
median5.2
Q39.9
95-th percentile15.93
Maximum65.8
Range65.2
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation5.1473611
Coefficient of variation (CV)0.80185562
Kurtosis4.7714146
Mean6.4193116
Median Absolute Deviation (MAD)3.6
Skewness1.1876275
Sum22005.4
Variance26.495326
MonotonicityNot monotonic
2023-04-13T17:03:47.920667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4 135
 
3.9%
1.2 129
 
3.8%
1.6 121
 
3.5%
1.1 109
 
3.2%
1.3 108
 
3.2%
1.5 97
 
2.8%
1.8 72
 
2.1%
1.7 71
 
2.1%
2 62
 
1.8%
1 58
 
1.7%
Other values (276) 2466
71.9%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 4
 
0.1%
0.8 16
 
0.5%
0.9 28
 
0.8%
0.95 4
 
0.1%
1 58
1.7%
1.1 109
3.2%
1.15 2
 
0.1%
1.2 129
3.8%
1.25 2
 
0.1%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
0.1%
26.05 1
< 0.1%
23.5 1
< 0.1%
22 1
< 0.1%
20.8 2
0.1%
20.7 2
0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%
20.2 1
< 0.1%

chlorides
Real number (ℝ)

Distinct142
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045636814
Minimum0.009
Maximum0.346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:47.980477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.027
Q10.036
median0.043
Q30.05
95-th percentile0.066
Maximum0.346
Range0.337
Interquartile range (IQR)0.014

Descriptive statistics

Standard deviation0.021631816
Coefficient of variation (CV)0.47399925
Kurtosis40.508837
Mean0.045636814
Median Absolute Deviation (MAD)0.007
Skewness5.133135
Sum156.443
Variance0.00046793546
MonotonicityNot monotonic
2023-04-13T17:03:48.037809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 153
 
4.5%
0.048 133
 
3.9%
0.036 129
 
3.8%
0.046 125
 
3.6%
0.04 124
 
3.6%
0.045 124
 
3.6%
0.034 123
 
3.6%
0.042 121
 
3.5%
0.037 115
 
3.4%
0.047 114
 
3.3%
Other values (132) 2167
63.2%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 1
 
< 0.1%
0.013 1
 
< 0.1%
0.014 3
 
0.1%
0.015 2
 
0.1%
0.016 5
0.1%
0.017 4
 
0.1%
0.018 9
0.3%
0.019 5
0.1%
0.02 11
0.3%
ValueCountFrequency (%)
0.346 1
< 0.1%
0.301 1
< 0.1%
0.29 1
< 0.1%
0.255 1
< 0.1%
0.239 1
< 0.1%
0.212 1
< 0.1%
0.209 1
< 0.1%
0.208 1
< 0.1%
0.204 1
< 0.1%
0.201 1
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct122
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.255543
Minimum3
Maximum146.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:48.098137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q123
median34
Q346
95-th percentile63
Maximum146.5
Range143.5
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.820055
Coefficient of variation (CV)0.47708967
Kurtosis1.7522122
Mean35.255543
Median Absolute Deviation (MAD)11
Skewness0.80003533
Sum120856
Variance282.91425
MonotonicityNot monotonic
2023-04-13T17:03:48.153950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 118
 
3.4%
26 89
 
2.6%
34 87
 
2.5%
31 86
 
2.5%
24 85
 
2.5%
36 84
 
2.5%
35 83
 
2.4%
23 77
 
2.2%
41 77
 
2.2%
27 76
 
2.2%
Other values (112) 2566
74.9%
ValueCountFrequency (%)
3 9
 
0.3%
4 8
 
0.2%
5 19
0.6%
6 21
0.6%
7 22
0.6%
8 27
0.8%
9 20
0.6%
10 42
1.2%
11 30
0.9%
11.5 1
 
< 0.1%
ValueCountFrequency (%)
146.5 1
 
< 0.1%
128 1
 
< 0.1%
124 1
 
< 0.1%
122.5 1
 
< 0.1%
118.5 1
 
< 0.1%
112 1
 
< 0.1%
110 1
 
< 0.1%
108 3
0.1%
105 1
 
< 0.1%
101 1
 
< 0.1%

total sulfur dioxide
Real number (ℝ)

Distinct240
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.19851
Minimum10
Maximum307.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:48.291552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile74
Q1109
median135
Q3167
95-th percentile211
Maximum307.5
Range297.5
Interquartile range (IQR)58

Descriptive statistics

Standard deviation41.984944
Coefficient of variation (CV)0.30380171
Kurtosis-0.20278256
Mean138.19851
Median Absolute Deviation (MAD)29
Skewness0.22211185
Sum473744.5
Variance1762.7355
MonotonicityNot monotonic
2023-04-13T17:03:48.343374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 49
 
1.4%
111 45
 
1.3%
117 42
 
1.2%
150 42
 
1.2%
98 41
 
1.2%
125 40
 
1.2%
126 40
 
1.2%
132 39
 
1.1%
149 39
 
1.1%
140 39
 
1.1%
Other values (230) 3012
87.9%
ValueCountFrequency (%)
10 1
 
< 0.1%
18 2
0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
24 2
0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
28 3
0.1%
29 1
 
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
307.5 1
 
< 0.1%
294 1
 
< 0.1%
259 1
 
< 0.1%
256 2
0.1%
253 2
0.1%
252 2
0.1%
251 3
0.1%
249.5 1
 
< 0.1%
249 2
0.1%
248 1
 
< 0.1%

density
Real number (ℝ)

Distinct811
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.994053
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:48.397722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.98964
Q10.99173
median0.9937
Q30.99612
95-th percentile0.999033
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00439

Descriptive statistics

Standard deviation0.003035075
Coefficient of variation (CV)0.0030532325
Kurtosis13.420468
Mean0.994053
Median Absolute Deviation (MAD)0.00211
Skewness1.2502655
Sum3407.6137
Variance9.2116801 × 10-6
MonotonicityNot monotonic
2023-04-13T17:03:48.454532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9928 47
 
1.4%
0.992 39
 
1.1%
0.9934 37
 
1.1%
0.9932 37
 
1.1%
0.9927 36
 
1.1%
0.9944 35
 
1.0%
0.9954 32
 
0.9%
0.9924 32
 
0.9%
0.9958 32
 
0.9%
0.9938 32
 
0.9%
Other values (801) 3069
89.5%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.9874 1
< 0.1%
0.98742 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
0.1%
0.98815 1
< 0.1%
0.98816 1
< 0.1%
0.98819 1
< 0.1%
ValueCountFrequency (%)
1.03898 1
< 0.1%
1.0103 2
0.1%
1.00295 1
< 0.1%
1.00241 1
< 0.1%
1.0024 1
< 0.1%
1.00196 1
< 0.1%
1.00182 1
< 0.1%
1.0017 2
0.1%
1.0012 1
< 0.1%
1.00118 1
< 0.1%

pH
Real number (ℝ)

Distinct98
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1892824
Minimum2.72
Maximum3.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:48.511869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.96
Q13.09
median3.18
Q33.28
95-th percentile3.45
Maximum3.82
Range1.1
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.14978697
Coefficient of variation (CV)0.046965727
Kurtosis0.50052533
Mean3.1892824
Median Absolute Deviation (MAD)0.1
Skewness0.41886512
Sum10932.86
Variance0.022436135
MonotonicityNot monotonic
2023-04-13T17:03:48.565689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14 125
 
3.6%
3.16 116
 
3.4%
3.24 105
 
3.1%
3.19 102
 
3.0%
3.15 98
 
2.9%
3.08 98
 
2.9%
3.22 97
 
2.8%
3.1 96
 
2.8%
3.12 94
 
2.7%
3.2 92
 
2.7%
Other values (88) 2405
70.2%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 1
 
< 0.1%
2.77 1
 
< 0.1%
2.79 3
0.1%
2.8 2
 
0.1%
2.83 2
 
0.1%
2.84 1
 
< 0.1%
2.85 3
0.1%
2.86 6
0.2%
2.87 6
0.2%
ValueCountFrequency (%)
3.82 1
 
< 0.1%
3.8 1
 
< 0.1%
3.77 2
0.1%
3.76 2
0.1%
3.75 1
 
< 0.1%
3.74 2
0.1%
3.72 3
0.1%
3.7 1
 
< 0.1%
3.69 1
 
< 0.1%
3.67 1
 
< 0.1%

sulphates
Real number (ℝ)

Distinct73
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48932905
Minimum0.22
Maximum1.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:48.624024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.34
Q10.41
median0.47
Q30.55
95-th percentile0.71
Maximum1.06
Range0.84
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.11344855
Coefficient of variation (CV)0.23184512
Kurtosis1.4286928
Mean0.48932905
Median Absolute Deviation (MAD)0.07
Skewness0.96000055
Sum1677.42
Variance0.012870574
MonotonicityNot monotonic
2023-04-13T17:03:48.677853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 181
 
5.3%
0.46 157
 
4.6%
0.44 152
 
4.4%
0.38 144
 
4.2%
0.45 130
 
3.8%
0.42 125
 
3.6%
0.4 121
 
3.5%
0.49 121
 
3.5%
0.54 118
 
3.4%
0.47 117
 
3.4%
Other values (63) 2062
60.2%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 3
 
0.1%
0.27 10
 
0.3%
0.28 9
 
0.3%
0.29 9
 
0.3%
0.3 16
0.5%
0.31 23
0.7%
0.32 36
1.1%
ValueCountFrequency (%)
1.06 1
 
< 0.1%
1 1
 
< 0.1%
0.98 3
0.1%
0.96 2
 
0.1%
0.95 5
0.1%
0.94 1
 
< 0.1%
0.92 2
 
0.1%
0.9 5
0.1%
0.89 1
 
< 0.1%
0.88 3
0.1%

alcohol
Real number (ℝ)

Distinct90
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.509993
Minimum8
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:48.734187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.9
Q19.5
median10.4
Q311.4
95-th percentile12.7
Maximum14.2
Range6.2
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.2296179
Coefficient of variation (CV)0.11699512
Kurtosis-0.6765175
Mean10.509993
Median Absolute Deviation (MAD)0.93333333
Skewness0.49611219
Sum36028.257
Variance1.5119602
MonotonicityNot monotonic
2023-04-13T17:03:48.788015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 164
 
4.8%
9.5 156
 
4.6%
9.2 136
 
4.0%
9 133
 
3.9%
10 118
 
3.4%
11 112
 
3.3%
10.4 111
 
3.2%
10.5 108
 
3.2%
9.1 102
 
3.0%
9.8 101
 
2.9%
Other values (80) 2187
63.8%
ValueCountFrequency (%)
8 1
 
< 0.1%
8.4 2
 
0.1%
8.5 6
 
0.2%
8.6 16
 
0.5%
8.7 57
1.7%
8.8 79
2.3%
8.9 66
1.9%
9 133
3.9%
9.1 102
3.0%
9.2 136
4.0%
ValueCountFrequency (%)
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 3
 
0.1%
13.9 2
 
0.1%
13.8 2
 
0.1%
13.7 6
 
0.2%
13.6 5
 
0.1%
13.5 8
0.2%
13.4 15
0.4%
13.3 4
 
0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8690198
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-04-13T17:03:48.835376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89256402
Coefficient of variation (CV)0.15208059
Kurtosis0.23542979
Mean5.8690198
Median Absolute Deviation (MAD)1
Skewness0.18785259
Sum20119
Variance0.79667053
MonotonicityNot monotonic
2023-04-13T17:03:48.870259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1530
44.6%
5 1031
30.1%
7 600
 
17.5%
8 126
 
3.7%
4 123
 
3.6%
3 13
 
0.4%
9 5
 
0.1%
ValueCountFrequency (%)
3 13
 
0.4%
4 123
 
3.6%
5 1031
30.1%
6 1530
44.6%
7 600
 
17.5%
8 126
 
3.7%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 126
 
3.7%
7 600
 
17.5%
6 1530
44.6%
5 1031
30.1%
4 123
 
3.6%
3 13
 
0.4%

Interactions

2023-04-13T17:03:46.588656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.140937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.809379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.512707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.153712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.868006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.532452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.280651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.927548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.626471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.246252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.934685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.643471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.202258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.863205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.568527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.209052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.925334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.591783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.335984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.981383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.679301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.301596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.990016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.693816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.255081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.912560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.619873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.260879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.979159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.645603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.388335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.032728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.728656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.349437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.053109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.746639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.311418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.964394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.672704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.314225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.033494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.700943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.443152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.084074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.780488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.402784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.110446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.797987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.368236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.016739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.727040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.366058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.086841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.759747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.495502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.135906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.831835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.453614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.163266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.852800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.424567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.072560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.780385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.421391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.143651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.818082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.551315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.189252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.885520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.505953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.218614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.906149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.481901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.127894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.834205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.477211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.198990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.876892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.605657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.242382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.938352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.558793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.274450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.959983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.537714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.179248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.888549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.528557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.253807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.933224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.657484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.292746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.992049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.609148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.327757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:47.012340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.593055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.308342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.942369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.581904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.306156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.990556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.717147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.345565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.042881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.659977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.379582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:47.068462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.646875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.359172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.994719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.712992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.358979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.122647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.770037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.395928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.093233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.709342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.431662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:47.118817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.700223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.407534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.045549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.762826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.419304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.174481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.821378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.525027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.144064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.756185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.481494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:47.249928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:39.755037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:40.463355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.098896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:41.815175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:42.476122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.226825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:43.876201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:44.575119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.194422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:45.885328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:46.535322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-13T17:03:48.913642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.0380.3130.1000.088-0.0260.1160.265-0.409-0.007-0.095-0.087
volatile acidity-0.0381.000-0.1300.101-0.004-0.0870.1160.015-0.034-0.0280.030-0.207
citric acid0.313-0.1301.0000.0270.0350.0850.1030.093-0.1430.086-0.0230.007
residual sugar0.1000.1010.0271.0000.2330.3500.4260.778-0.175-0.013-0.442-0.085
chlorides0.088-0.0040.0350.2331.0000.1710.3700.508-0.0540.092-0.568-0.315
free sulfur dioxide-0.026-0.0870.0850.3500.1711.0000.6230.3350.0060.038-0.2770.032
total sulfur dioxide0.1160.1160.1030.4260.3700.6231.0000.564-0.0060.147-0.475-0.194
density0.2650.0150.0930.7780.5080.3350.5641.000-0.1120.093-0.820-0.350
pH-0.409-0.034-0.143-0.175-0.0540.006-0.006-0.1121.0000.1380.1560.125
sulphates-0.007-0.0280.086-0.0130.0920.0380.1470.0930.1381.000-0.0500.038
alcohol-0.0950.030-0.023-0.442-0.568-0.277-0.475-0.8200.156-0.0501.0000.441
quality-0.087-0.2070.007-0.085-0.3150.032-0.194-0.3500.1250.0380.4411.000

Missing values

2023-04-13T17:03:47.329190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-13T17:03:47.408460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
06.60.250.3014.400.05240.0183.00.998003.020.509.106
17.80.260.493.200.02728.087.00.991903.030.3211.307
26.30.230.331.500.03615.0105.00.991003.320.4211.206
36.00.260.187.000.05550.0194.00.995913.210.439.005
47.90.370.312.850.0375.024.00.991103.190.3611.906
56.20.330.144.800.05227.0128.00.994753.210.489.405
66.30.410.227.300.03523.0117.00.991723.200.3911.947
78.30.220.3814.800.05432.0126.01.000203.220.509.705
87.40.240.261.600.05853.0150.00.993603.180.509.907
96.50.270.196.600.04598.0175.00.993643.160.3410.106
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
34187.20.250.2814.400.05555.0205.00.998603.120.389.07
34195.70.260.2510.400.0207.057.00.994003.390.3710.65
34206.40.160.328.750.03838.0118.00.994493.190.4110.75
34217.30.200.392.300.04824.087.00.990442.940.3512.06
34226.70.300.4418.750.05765.0224.00.999563.110.539.15
34236.20.210.526.500.04728.0123.00.994183.220.499.96
34247.00.140.329.000.03954.0141.00.995603.220.439.46
34257.60.270.523.200.04328.0152.00.991293.020.5311.46
34266.30.240.2913.700.03553.0134.00.995673.170.3810.66
34278.10.270.351.700.03038.0103.00.992553.220.6310.48

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
2317.00.150.2814.700.05129.0149.00.997922.960.399.077
3037.30.190.2713.900.05745.0155.00.998072.940.418.887
3247.40.190.3114.500.04539.0193.00.998603.100.509.266
185.70.220.2016.000.04441.0113.00.998623.220.468.965
3507.60.200.3014.200.05653.0212.50.999003.140.468.985
195.70.220.2216.650.04439.0110.00.998553.240.489.064
636.20.230.3617.200.03937.0130.00.999463.230.438.864
656.20.250.547.000.04658.0176.00.994543.190.7010.454
1606.70.160.3212.500.03518.0156.00.996662.880.369.064
1856.80.180.289.800.03929.0113.00.994063.110.4510.974